During the analysis of medical images, it is often important to segment a particular organ or other anatomical structure from the rest of the image. One way of performing this segmentation is by generating a contour around the anatomical structure. The term “contour,” as used herein refers to an outline representing or bounding the shape or form of the anatomical object. Conventional techniques exist for performing contouring of images in a manual or intelligent manner.
Manual contouring tools include painting, freehand curve drawing, click-point based polygon drawing, nudge tool, etc., all depending on different user inputs. Manual contouring tools allow a user to generate precise contouring around objects. However, it is often tedious and time-consuming to use manual tool for precise contouring. For example, conventional click-point based contouring techniques often require the user to make many click points around the anatomical structure to ensure that the contour is properly defined.
Intelligent contouring (or intelligent editing) tries to overcome the limitation of manual contouring by invoking intelligent computer algorithms that utilizes the image information to infer the contour. User inputs are provided to guide such an inference in the hope that the final contouring with limited number of user inputs converges to the target. However, sometime even with a large number of user inputs, the intelligent editing fails to converge, thereby leading to editing inefficiency. Examples of intelligent contouring algorithms include intelligent scissor, livewire, smart brush, random walk, and interactive graph cuts.